Imaging and tracking systems typically include sensors to identify and track objects. For example, some sensors, such as radar systems, send out signals that reflect from objects and are received by the system. Other sensors, such as electro-optical sensors, receive electromagnetic radiation signals from the objects themselves. Improvements in this field have been directed to refining these sensors to be more accurate.
In particular, electro-optical sensors typically use telescopes and focal plane arrays that detect infrared radiation. Suppression of fixed pattern noise is one area of development in electro-optical sensors. Generally, calibration or non-uniformity correction has been used to suppress fixed pattern noise. However, this method of fixed pattern suppression may leave a large residual fixed pattern which limits sensor performance and increases sensor noise levels. In addition, there may be instances when calibration cannot be performed prior to use, and the system must be used in a moment's notice.
Tracking objects using an optical sensor with a telescope and focal plane array on a moving platform presents additional problems, such as, a need to compensate for the movement of the moving platform. Furthermore, optical distortion caused by the system's optical components, such as from imperfect lenses, reflectors, etc., may cause localized stretching within the image. Correction of each of these problems using separate processes may contribute to an increase in unwanted random noise related to the optical sensor and associated electronics. The signal-to-noise ratio (SNR) of the system quantifies the amount of object signal or object intensity that is present above any unwanted random noise. A higher SNR generally correlates to a higher probability for detecting the object of interest. Accordingly, improvements in electro-optical sensors are needed to suppress fixed pattern noise and increase the system's SNR.